Evidence map›Paper›PMID 39897550›Full record

ReviewTheranostics2025

Current status, challenges, and prospects of artificial intelligence applications in wound repair theranostics.

Huazhen Liu, Wenbin Sun, Weihuang Cai, Kaidi Luo, Chunxiang Lu, Aoxiang Jin, Jiantao Zhang, Yuanyuan Liu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Theranostics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed.

  1. Review
  2. Artificial Intelligence in Burn and Wound Care: Image Analysis, Prediction, and Clinical Integration.Journal of burn care & research : official publication of the American Burn Association · 2026
    Review
  3. Review
  4. Review
  5. Article
  6. Article
  7. Review
  8. Advances in Remote Monitoring Technology Applications in Anesthesia: A Narrative Review.Medical science monitor : international medical journal of experimental and clinical research · 2026
    Review
  9. Review
  10. Review
  11. Review
  12. Review
  13. Review
  14. Article
  15. Article
  16. Review
  17. Review
  18. Observational
  19. Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Huazhen LiuSchool of Medicine, Shanghai University, Shanghai, 200444, People's Republic of China.
Wenbin SunSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai, People's Republic of China.
Weihuang CaiSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai, People's Republic of China.
Kaidi LuoSchool of Medicine, Shanghai University, Shanghai, 200444, People's Republic of China.
Chunxiang LuSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai, People's Republic of China.
Aoxiang JinSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai, People's Republic of China.
Jiantao ZhangSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai, People's Republic of China.
Yuanyuan LiuSchool of Medicine, Shanghai University, Shanghai, 200444, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Skin injuries caused by physical, pathological, and chemical factors not only compromise appearance and barrier function but can also lead to life-threatening microbial infections, posing significant challenges for patients and healthcare systems. Artificial intelligence (AI) technology has demonstrated substantial advantages in processing and analyzing image information. Recently, AI-based methods and algorithms, including machine learning, deep learning, and neural networks, have been extensively explored in wound care and research, providing effective clinical decision support for wound diagnosis, treatment, prognosis, and rehabilitation. However, challenges remain in achieving a closed-loop care system for the comprehensive application of AI in wound management, encompassing wound diagnosis, monitoring, and treatment. This review comprehensively summarizes recent advancements in AI applications in wound repair. Specifically, it discusses AI's role in injury type classification, wound measurement (including area and depth), wound tissue type classification, wound monitoring and prediction, and personalized treatment. Additionally, the review addresses the challenges and limitations AI faces in wound management. Finally, recommendations for the application of AI in wound repair are proposed, along with an outlook on future research directions, aiming to provide scientific evidence and technological support for further advancements in AI-driven wound repair theranostics.

Indexed as

Artificial IntelligenceTheranostic NanomedicineWound HealingWounds and InjuriesDeep LearningHumansMachine LearningNeural Networks, ComputerPrecision MedicineSkinArtificial intelligenceDeep learningSkin injuriesWound diagnosisWound repair

Identifiers

PMID39897550
PMCPMC11780524

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.